This paper investigates the filtering techniques to enhance the accuracy of indoor localization for Autonomous Guided Vehicles (AGVs) using Ultra-Wideband (UWB) technology. A comprehensive comparative analysis of various filtering approaches, including the Kalman Filter (KF), Moving Average Filter (MA), Savitzky-Golay Filter (SG), Weighted Average Filter (WAF), and their combinations, are conducted. The primary focus of this paper is the integration of a Moving Average-Kalman Filter (MAKF) with an extended window size of 201. Experimental findings reveal significant performance differences among these filtering techniques. The most effective approach is the MAKF technique, achieving an accuracy of 85.13% and the lowest path deviation of 0.17 meters. Conversely, the MA exhibits the lowest accuracy at 68.83%. Notably, the WAF attains an accuracy of 72.46% but exhibits a significantly higher path deviation of 2.65 meters compared to 1.45 meters of the MA filtering technique. The proposed MAKF acknowledged for its ability to effectively reduce noise with real-time responsiveness, represents a significant advancement in AGV indoor localization techniques